Triple
T7950923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Almost Famous |
E184610
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Lisa Stewart
Lisa Stewart is a film producer best known for her work on the acclaimed coming-of-age movie "Almost Famous."
|
E802616
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Lisa Stewart | Statement: [Almost Famous, producer, Lisa Stewart]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lisa Stewart Context triple: [Almost Famous, producer, Lisa Stewart]
-
A.
Lisa Rogers
Lisa Rogers is a member of the Rogers family, known as the daughter of Canadian businessman and media magnate Ted Rogers.
-
B.
Karen Stanton
Karen Stanton is a British academic leader and university administrator who has served as vice-chancellor of York St John University.
-
C.
Lisa Baird
Lisa Baird is an American sports executive who served as commissioner of the National Women's Soccer League (NWSL).
-
D.
Kimberly Steward
Kimberly Steward is an American film producer best known for her work on acclaimed independent films, including the Oscar-winning drama "Manchester by the Sea."
-
E.
Alana Stewart
Alana Stewart is an American actress, former model, and television personality known for her work in film and TV as well as her high-profile marriages and advocacy, including cancer-related causes.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lisa Stewart Triple: [Almost Famous, producer, Lisa Stewart]
Generated description
Lisa Stewart is a film producer best known for her work on the acclaimed coming-of-age movie "Almost Famous."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lisa Stewart Target entity description: Lisa Stewart is a film producer best known for her work on the acclaimed coming-of-age movie "Almost Famous."
-
A.
Lisa Rogers
Lisa Rogers is a member of the Rogers family, known as the daughter of Canadian businessman and media magnate Ted Rogers.
-
B.
Karen Stanton
Karen Stanton is a British academic leader and university administrator who has served as vice-chancellor of York St John University.
-
C.
Lisa Baird
Lisa Baird is an American sports executive who served as commissioner of the National Women's Soccer League (NWSL).
-
D.
Kimberly Steward
Kimberly Steward is an American film producer best known for her work on acclaimed independent films, including the Oscar-winning drama "Manchester by the Sea."
-
E.
Alana Stewart
Alana Stewart is an American actress, former model, and television personality known for her work in film and TV as well as her high-profile marriages and advocacy, including cancer-related causes.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca8292cba881908a64427b938dac47 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b5b7450819091e4e6f21e9d832d |
completed | March 31, 2026, 3:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12cabbb088190bada143db831c466 |
completed | April 4, 2026, 3:22 p.m. |
| NEDg | Description generation | batch_69d12e3da2848190b35cf2472c78e7a6 |
completed | April 4, 2026, 3:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d12e7f03f881908f172b13bc9897d6 |
completed | April 4, 2026, 3:30 p.m. |
Created at: March 30, 2026, 5:10 p.m.